Oguz
Mulayim
Contract Researcher
Learning Systems
(+34) 93 580 9570 ext.
431845
oguz@iiia.csic.es
Research areas:
- Citizen Science
- Deep Learning
- Machine Learning
- Probabilistic Graphical Models
- Probabilistic Learning
- Case-Based Reasoning
Impact areas:
- Smart Cities
- Social Networking
- Healthcare
SDGs:
- GOAL 11: Sustainable Cities and Communities
- GOAL 13: Climate Action
- SDGs: General
- GOAL 03: Good Health and Well-being
- GOAL 16: Peace, Justice and Strong Institutions
- GOAL 06: Clean Water and Sanitation
In Press
Hafiz Budi Firmansyah,
Valerio Lorini,
Oguz Mulayim,
Jorge Gomes,
& Jose Luis Fernandez Marquez
(In Press).
Improving Social Media Geolocation for Disaster Response by Using Text From Images and ChatGPT.
Accepted at MISNC 2024, The 11th Multidisciplinary International Social Networks Conference, August 21-23, 2024
.
[BibTeX]
2024
Hafiz Budi Firmansyah,
Jose Luis Fernandez Marquez,
Oguz Mulayim,
Jorge Gomes,
& Valerio Lorini
(2024).
Accelerating Crisis Response: Automated Image Classification for Geolocating Social Media Content.
Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
(pp. 77–81).
Association for Computing Machinery.
https://doi.org/10.1145/3625007.3627831.
[BibTeX]
[PDF]
Rocco Ballester,
Yanis Labeyrie,
Oguz Mulayim,
Jose Luis Fernandez Marquez,
& Jesus Cerquides
(2024).
Crowdsourced geolocation: Detailed exploration of mathematical and computational modeling approaches.
Cognitive Systems Research, 88, 101266.
https://doi.org/10.1016/j.cogsys.2024.101266.
[BibTeX]
Hafiz Budi Firmansyah,
Jose Luis Fernandez Marquez,
Oguz Mulayim,
Jorge Gomes,
Joao Ribeiro,
& Valerio Lorini
(2024).
Empowering Crisis Response Efforts: A Novel Approach to Geolocating Social Media Images for Enhanced Situational Awareness.
ISCRAM Proceedings, 21.
https://ojs.iscram.org/index.php/Proceedings/article/view/21.
[BibTeX]
[PDF]
2023
Carlo Bono,
Oguz Mulayim,
Cinzia Cappiello,
Mark James Carman,
Jesus Cerquides,
Jose Luis Fernandez Marquez,
Maria Rosa Mondardini,
Edoardo Ramalli,
& Barbara Pernici
(2023).
A Citizen Science Approach for Analyzing Social Media With Crowdsourcing.
IEEE Access, 11, 15329-15347.
https://doi.org/10.1109/ACCESS.2023.3243791.
[BibTeX]
[PDF]
Carlo Bono,
Oguz Mulayim,
& Barbara Pernici
(2023).
Learning Early Detection of Emergencies from Word Usage Patterns on Social Media.
Terje Gjøsæter, Jaziar Radianti, & Yuko Murayama (Eds.),
Information Technology in Disaster Risk Reduction
(pp. 308--323).
Springer Nature Switzerland.
https://doi.org/10.1007/978-3-031-34207-3_20.
[BibTeX]
[PDF]
Rocco Ballester,
Yanis Labeyrie,
Oguz Mulayim,
Jose Luis Fernandez Marquez,
& Jesus Cerquides
(2023).
Mathematical and Computational Models for Crowdsourced Geolocation.
Ismael Sanz, Raquel Ros, & Jordi Nin (Eds.),
Frontiers in Artificial Intelligence and Applications, Vol. 375: Artificial Intelligence Research and Development (pp 301--310).
IOS Press.
https://doi.org/10.3233/FAIA230699.
[BibTeX]
[PDF]
2022
Fabio Murgese,
Gerard Alcaina,
Oguz Mulayim,
Jesus Cerquides,
& Jose Luis Fernandez Marquez
(2022).
Automatic Outdoor Image Geolocation with Focal Modulation Networks.
Atia Cortés, Francisco Grimaldo, & Tommaso Flaminio (Eds.),
Frontiers in Artificial Intelligence and Applications, Vol. 356: Artificial Intelligence Research and Development (pp 279--288).
IOS Press.
https://doi.org/10.3233/FAIA220349.
[BibTeX]
[PDF]
Jesus Cerquides
(2022).
crowdnalysis: A software library to help analyze crowdsourcing results.
https://doi.org/10.5281/zenodo.5898579.
[BibTeX]
Barbara Pernici,
Carlo Bono,
Jose Luis Fernandez Marquez,
& Oguz Mulayim
(2022).
The Challenge of Collecting and Analyzing Information from Citizens and Social Media in Emergencies: The Crowd4SDG Experience and Tools.
Renata Guizzardi, Jolita Ralyté, & Xavier Franch (Eds.),
Research Challenges in Information Science: 16th International Conference, RCIS 2022, Proceedings
(pp. 823-824).
Springer.
[BibTeX]
[PDF]
Carlo Bono,
Barbara Pernici,
Jose Luis Fernandez Marquez,
Amudha Ravi Shankar,
Oguz Mulayim,
& Edoardo Nemni
(2022).
TriggerCit: Early Flood Alerting using Twitter and Geolocation - a comparison with alternative sources.
Rob Grace, & Hossein Baharmand (Eds.),
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management
(pp. 674--686).
[BibTeX]
[PDF]
2021
Jesus Cerquides,
Oguz Mulayim,
Jeronimo Hernandez-Gonzalez,
Amudha Ravi Shankar,
& Jose Luis Fernandez Marquez
(2021).
A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data.
Mathematics, 9.
https://doi.org/10.3390/math9080875.
[BibTeX]
[PDF]
2020
Oguz Mulayim
(2020).
Anytime Lazy kNN (ALK): A fast anytime kNN search algorithm.
https://doi.org/10.5281/zenodo.4472641.
[BibTeX]
Oguz Mulayim,
& Josep Lluis Arcos
(2020).
Fast anytime retrieval with confidence in large-scale temporal case bases.
Knowledge-Based Systems, 206, 106374.
https://doi.org/10.1016/j.knosys.2020.106374.
[BibTeX]
[PDF]
2019
David Sanchez-Pinsach,
Oguz Mulayim,
Jennifer Grau-Sánchez,
Emma Segura,
Berta Juan-Corbella,
Josep Lluis Arcos,
Jesus Cerquides,
Monique Messaggi-Sartor,
Esther Duarte,
& Antoni Rodriguez-Fornells
(2019).
Design of an AI Platform to Support Home-Based Self-Training Music Interventions for Chronic Stroke Patients.
Jordi Sabater-Mir, Vicenç Torra, Isabel Aguilo, & Manuel González-Hidalgo (Eds.),
Frontiers in Artificial Intelligence and Applications (pp 170--175).
IOS Press.
https://doi.org/10.3233/FAIA190120.
[BibTeX]
2018
Oguz Mulayim,
& Josep Lluis Arcos
(2018).
Perks of Being Lazy: Boosting Retrieval Performance.
Twenty-Sixth International Conference on Case-Based Reasoning
.
https://doi.org/10.1007/978-3-030-01081-2_21.
[BibTeX]
2011
Josep Lluis Arcos,
Oguz Mulayim,
& David Leake
(2011).
Using introspective reasoning to improve CBR system performance.
M. T. Cox, & A. Raja (Eds.),
Metareasoning: Thinking about Thinking (pp 167-182).
MIT Press.
https://doi.org/10.7551/mitpress/9780262014809.003.0011.
[BibTeX]
2010
Oguz Mulayim,
& Josep Lluis Arcos
(2010).
Predicting Dubiosity in CBR Systems.
Expert UPDATE, 10, 1-8.
http://www.expertupdate.org/papers/10-2/1.pdf.
[BibTeX]
[PDF]
2008
Oguz Mulayim,
& Josep Lluis Arcos
(2008).
Understanding Dubious Future Problems.
D Althoff, R Bergmann, M Minor, & A Hanft (Eds.),
Advances in Case-Based Reasoning: 9th European Conference, ECCBR 2008. Lecture Notes in Artificial Intelligence
(pp. 385-399).
Springer Verlag.
https://doi.org/10.1007/978-3-540-85502-6_26.
[BibTeX]
[PDF]
Josep Lluis Arcos,
Oguz Mulayim,
& David Leake
(2008).
Using Introspective Reasoning to Improve CBR System Performance.
Michael T. Cox, & Anita Raja (Eds.),
AAAI Metareasoning Workshop
(pp. 21-28).
AAAI Press.
[BibTeX]
[PDF]
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